在日常生活中可以發現時常會想要探討兩變項間的關係性，其中兩連續變項的關係性為相關性，通常以散布圖來呈現數據；兩類別變項的關係性為關聯性，通常會以列聯表呈現數據。此研究最主要是想探討這兩者間的關係為何。
先前對於相關性的研究大多皆為在探討學生對於相關的迷思概念，尚未有探討學生對相關係數公式的關係性理解，因此此研究想探討學生對於相關係數公式的關係性理解和相關係數公式的理解間的關聯性為何。另外，有許多研究探討學生對於列聯表關聯性推理的策略與迷思概念有哪些，其中也指出關聯性推理能力並非直觀的能力，但小學生可以進行自發性的推理。因此此研究想瞭解學生是否可以成功將相關概念與公式的理解轉化到列聯表的關聯性推理。
此研究發現相關係數公式的理解和相關概念的理解彼此相輔相成，因此在教學中可以以關係性理解的方式進行相關係數公式的教學，如此可以幫助學生能更理解相關概念，並且可以減少產生迷思概念。而且此研究發現若相關概念與公式的理解夠清楚的話，學生的列聯表關聯性推理也會較能判斷正確。若能加強這個部分，而非只是採取條件分布的關聯性推理策略進行推理的話，學生在判斷不同數字結構的列聯表關聯性時，便不會受數字結構的影響而皆能判斷正確。另一方面，此研究還發現統計不確定性概念是很重要的判斷因素，若能加強學生對統計不確定性概念的話，即使是以條件分布的關聯性推理策略來進行推理的話，也較能判斷正確。

一、中文部分
林怡均（2005）。高中生「相關」迷思概念診斷工具之發展歷程研究（博士論文）。取自http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=9212700792127007
江美菊（2012）。相關係數面面觀. 政治大學應用數學系數學教學碩士在職專班學位論文, 1-30.
楊凱琳（2015）。提升臺灣K-12學生數學素養之研究－子計畫六：整合閱讀與建模的高中學生統計素養培育活動探討。科技部專題研究期中報告（編號：MOST 104-2511-S-003-006-MY3），未出版。
藍郅堯（2017）。探討不同年級學生對列聯表資料推理兩變項關係的表現（碩士論文）。（尚未公開）
劉子鍵、林怡均（2011）。發展二階段診斷工具探討學生之統計迷思概念: 以 [相關] 為例。教育心理學報, 42(3), 379-399.
莫爾、諾茨 （2012）。統計學的世界（第二版）（鄭惟厚譯）。臺北市﹕天下遠見出版股份有限公司。（原著出版年﹕1997年）

二、英文部分
Allan, L. G., & Jenkins, H. M. (1983). The effect of representations of binary variables on judgment of influence. Learning and Motivation, 14(4), 381-405.
Anand, V. (2007). A study of time management: The correlation between video game usage and academic performance markers. CyberPsychology & Behavior, 10(4), 552-559.
Batanero, C., Estepa, A., Godino, J. D., & Green, D. R. (1996). Intuitive strategies and preconceptions about association in contingency tables. Journal for Research in Mathematics Education, 27, 151–169.
Batanero, C., Estepa, A., & Godino, J. D. (1997). Evolution of students’ understanding of statistical association in a computer based teaching environment. Research on the role of technology in teaching and learning statistics, 191-205.
Batanero, C., Godino, J., & Estepa, A. (1998). Building the meaning of statistical association through data analysis activities. In A. Olivier, & K. Newstead (Eds.), Proceedings of the 22nd Conference of the International Group for the Psychology of Mathematics Education (Vol. 1, pp. 221-236). Stellenbosh, South Africa: University of Stellenbosh.
Batanero, C., Cañadas, G. R., Arteaga, P., & Gea, M. M. (2013). Psychology students’ strategies and semiotic conflicts when assessing independence. In Proceedings of the eighth congress of the european society for research in mathematics education(pp. 756-765).
Chapman, L. J., & Chapman, J. P. (1969). Illusory correlation as an obstacle to the use of valid psychodiagnostic signs. Journal of abnormal psychology, 74(3), 271.
Estepa, A., & Batanero, C. (1996). Judgments of correlation in scatterplots: Students’ intuitive strategies and preconceptions. Hiroshima Journal of Mathematics Education, 4(25).
Garfield, J., & Chance, B. (2000). Assessment in statistics education: Issues and challenges. Mathematical Thinking and Learning, 2(1-2), 99-125.
Garfield, J. B. (2003). Assessing statistical reasoning. Statistics Education Research Journal, 2(1), 22-38.
Hiebert, J., & Carpenter, T. P. (1992). Learning and teaching with understanding. Handbook of research on mathematics teaching and learning: A project of the National Council of Teachers of Mathematics, 65-97.
Jenkins, H. M., & Ward, W. C. (1965). Judgment of contingency between responses and outcomes. Psychological monographs: General and applied, 79(1), 1.
Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59-66.
Lovett, M. C., & Greenhouse, J. B. (2000). Applying cognitive theory to statistics instruction. The American Statistician, 54(3), 196-206.
Michener, E. R. (1978). Understanding understanding mathematics. Cognitive science, 2(4), 361-383.
No, A. R., Han, S. Y., & Yoo, Y. J. (2016). Korean High School Students’ Understanding of the Concept of Correlation. In The Teaching and Learning of Statistics (pp. 71-81). Springer, Cham.
Obersteiner, A., Bernhard, M., & Reiss, K. (2015). Primary school children’s strategies in solving contingency table problems: The role of intuition and inhibition. ZDM Mathematics Education, 47, 825–836.
Pérez Echeverría, M. P.(1990). Psicología del razonamiento probabilístico, Madrid: Ediciones de la Universidad Autónoma de Madrid.
Piaget, J., & Inhelder, B. (1975). The origin of the idea of chance in children.
Pirie, S., & Kieren, T. (1994). Growth in mathematical understanding: How can we characterise it and how can we represent it?. In Learning Mathematics (pp. 61-86). Springer, Dordrecht.
Phillips, D. H., Hewer, A., Martin, C. N., Garner, R. C., & King, M. M. (1988). Correlation of DNA adduct levels in human lung with cigarette smoking. Nature, 336(6201), 790.
Shaklee, H., & Mims, M. (1982). Sources of error in judging event covariations: Effects of memory demands. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8(3), 208.
Skemp, R. R. (1976). Relational understanding and instrumental understanding. Mathematics teaching, 77(1), 20-26.
Tall, D., & Vinner, S. (1981). Concept image and concept definition in mathematics with particular reference to limits and continuity. Educational studies in mathematics, 12(2), 151-169.
Wang, F.-J., & Yang, K.-L. (2017). Exploratory study on the misconceptions of the Pearson correlation in relation to its formula. In Kaur, B., Ho, W.K., Toh, T.L., & Choy, B.H. (Eds.). Proceedings of the 41st Conference of the International Group for the Psychology of Mathematics Education, Vol. 1, p. 284. Singapore: PME.
Wang, F. J., Yang, K. L., & Liu, X. Y. (2018). Exploratory study on the relation between the understanding of correlation and association. Proceedings of the 42st Conference of the International Group for the Psychology of Mathematics Education, Vol. 5, pp. 187. Umea, Sweden: PME.
Zieffler, A. S. (2006). A longitudinal investigation of the development of college students' reasoning about bivariate data during an introductory statistics course (Doctoral dissertation, University of Minnesota).